Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning Article Swipe
Ana Paula Oliveira Muller
,
Jessé C. Costa
,
C. R. Bom
,
Elisângela L. Faria
,
Matheus Klatt
,
Gabriel Teixeira
,
Marcelo P. de Albuquerque
,
Márcio P. de Albuquerque
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.09710
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.09710
Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.09710
- https://arxiv.org/pdf/2204.09710
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224287979
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224287979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.09710Digital Object Identifier
- Title
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Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-20Full publication date if available
- Authors
-
Ana Paula Oliveira Muller, Jessé C. Costa, C. R. Bom, Elisângela L. Faria, Matheus Klatt, Gabriel Teixeira, Marcelo P. de Albuquerque, Márcio P. de AlbuquerqueList of authors in order
- Landing page
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https://arxiv.org/pdf/2204.09710Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2204.09710Direct OA link when available
- Concepts
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Offset (computer science), Convolutional neural network, Computer science, Benchmark (surveying), Artificial intelligence, Deep learning, Geology, Pattern recognition (psychology), Salt (chemistry), Algorithm, Geodesy, Chemistry, Physical chemistry, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.migrated | 4, 32 |
| abstract_inverted_index.offsets. | 91 |
| abstract_inverted_index.produced | 34 |
| abstract_inverted_index.sediment | 45 |
| abstract_inverted_index.training | 147 |
| abstract_inverted_index.velocity | 38 |
| abstract_inverted_index.accuracy; | 128 |
| abstract_inverted_index.benchmark | 138 |
| abstract_inverted_index.moreover, | 129 |
| abstract_inverted_index.partially | 162 |
| abstract_inverted_index.performed | 132 |
| abstract_inverted_index.synthetic | 93, 137 |
| abstract_inverted_index.velocity, | 46 |
| abstract_inverted_index.Delimiting | 0 |
| abstract_inverted_index.available. | 27 |
| abstract_inverted_index.inaccurate | 37 |
| abstract_inverted_index.inclusions | 2, 56, 124 |
| abstract_inverted_index.previously | 144 |
| abstract_inverted_index.reasonable | 42 |
| abstract_inverted_index.salt-masks | 113 |
| abstract_inverted_index.sediments' | 75 |
| abstract_inverted_index.subsurface | 68, 102, 164 |
| abstract_inverted_index.inclusions) | 50 |
| abstract_inverted_index.introduced. | 145 |
| abstract_inverted_index.limitations | 23 |
| abstract_inverted_index.reflections | 76, 88 |
| abstract_inverted_index.successfully | 153 |
| abstract_inverted_index.Convolutional | 60 |
| abstract_inverted_index.approximation | 43 |
| abstract_inverted_index.common-offset | 101 |
| abstract_inverted_index.human-curated | 14 |
| abstract_inverted_index.interpretation | 20 |
| abstract_inverted_index.time-consuming | 8 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.06648199 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |